RNA-Seq: differential expression analysis of controls (lab + literature)
Libraries required
library(limma)
library(edgeR)
library(plgINS)
library(sva)
library(ggplot2)
library(dplyr)
library(patchwork)
library(SummarizedExperiment)
library(reshape2)
library(autoplotly)
library(pheatmap)
library(viridis)
library(RColorBrewer)
library(ggplotify)
library(SEtools)
library(DESeq2)Load salmon object
load("input/SC_controls_rnaseq_lab_june2021.tds.RData")Differeitial analysis using limma
PND8 vs PND15
design <- model.matrix(~ 0 + Group + LibPrepBatch,
data = salmon@phenoData[grep(pattern = "PND8|PND15", x = salmon@phenoData$Group), ]
)
colnames(design) <- gsub(pattern = "Group", replacement = "", x = colnames(design))
y <- DGEList(counts = salmon@gene.counts[, grep(pattern = "PND8|PND15", x = colnames(salmon@gene.counts))])
keep <- filterByExpr(y, design, min.count = 15)
y <- y[keep, ]
en <- as.matrix(vst(round(y$counts), blind = TRUE))## converting counts to integer modesv <- sva(en, design, n.sv = NULL)$sv## Number of significant surrogate variables is: 3
## Iteration (out of 5 ):1 2 3 4 5colnames(sv) <- paste0("SV", 1:ncol(sv))
design <- cbind(sv, design)
dds <- calcNormFactors(y)
v <- voom(dds, design = design)
contrast.matrix <- makeContrasts(PND15 - PND8, levels = design)
fit <- lmFit(v)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
pnd8.pnd15 <- as.data.frame(topTable(fit2, coef = 1, number = Inf))
pnd8.pnd15 <- data.frame(Genes = rownames(pnd8.pnd15), pnd8.pnd15, stringsAsFactors = F)
pnd8.pnd15_sig <- pnd8.pnd15[abs(pnd8.pnd15$logFC) >= 1 & pnd8.pnd15$adj.P.Val <= 0.05, ]PND15 vs PNW21
design <- model.matrix(~ 0 + Group + LibPrepBatch,
data = salmon@phenoData[grep(pattern = "PND15|PNW21", x = salmon@phenoData$Group), ]
)
colnames(design) <- gsub(pattern = "Group", replacement = "", x = colnames(design))
y <- DGEList(counts = salmon@gene.counts[, grep(pattern = "PND15|PNW21", x = colnames(salmon@gene.counts))])
keep <- filterByExpr(y, design, min.count = 15)
y <- y[keep, ]
en <- as.matrix(vst(round(y$counts), blind = TRUE))## converting counts to integer modesv <- sva(en, design, n.sv = NULL)$sv## Number of significant surrogate variables is: 3
## Iteration (out of 5 ):1 2 3 4 5colnames(sv) <- paste0("SV", 1:ncol(sv))
design <- cbind(sv, design)
dds <- calcNormFactors(y)
v <- voom(dds, design = design)## Coefficients not estimable: LibPrepBatchB2## Warning: Partial NA coefficients for 20832 probe(s)contrast.matrix <- makeContrasts(PNW21 - PND15, levels = design)
fit <- lmFit(v)## Coefficients not estimable: LibPrepBatchB2## Warning: Partial NA coefficients for 20832 probe(s)fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)
pnd15.pnw21 <- as.data.frame(topTable(fit2, coef = 1, number = Inf))
pnd15.pnw21 <- data.frame(Genes = rownames(pnd15.pnw21), pnd15.pnw21, stringsAsFactors = F)
pnd15.pnw21_sig <- pnd15.pnw21[abs(pnd15.pnw21$logFC) >= 1 & pnd15.pnw21$adj.P.Val <= 0.05, ]cpm Counts data and pData
y <- DGEList(counts = salmon@gene.counts)
keep <- filterByExpr(y, design, min.count = 15)
y <- y[keep, ]
design <- model.matrix(~ 0 + Group + LibPrepBatch, data = salmon@phenoData)
sv <- svacor(y$counts, mm = design)## Using blind variance-stabilizing transformation## converting counts to integer mode## Number of significant surrogate variables is: 5
## Iteration (out of 5 ):1 2 3 4 5dds <- calcNormFactors(y, design = design)
cpm <- cpm(dds, log = T)
data <- list(cpm = cpm, sva = sv$cor, pData = salmon@phenoData)
save(data,
file = "./output/data_pData.RData", compress = T,
compression_level = 3
)Results
dea.list <- list(
`pnd15 vs pnd8` = as.DEA(pnd8.pnd15),
`pnw21 vs pnd15` = as.DEA(pnd15.pnw21)
)
dea.limma <- list(
`pnd15 vs pnd8` = pnd8.pnd15,
`pnw21 vs pnd15` = pnd15.pnw21
)Save RData files
save(dea.list,
file = "./output/dea_SC_Controls_lab_newSeq.DEA.RData", compress = T,
compression_level = 3
)
save(dea.limma,
file = "./output/limma_SC_Controls_lab_newSeq.RData", compress = T,
compression_level = 3
)
writexl::write_xlsx(x = dea.limma, path = "output/dea_results.xlsx", col_names = T, format_headers = T)MA plots
PND15 vs PND8
res_8_15 <- pnd8.pnd15
res_8_15$threshold <- as.factor(res_8_15$adj.P.Val < 0.05)
p1 <- ggplot(data = res_8_15, aes(
x = res_8_15$AveExpr,
y = res_8_15$logFC,
colour = threshold
)) +
geom_point(alpha = 0.5, size = 1.8) +
geom_hline(aes(yintercept = 0), colour = "blue", size = 1) +
ylim(c(
-ceiling(max(abs(res_8_15$logFC))),
ceiling(max(abs(res_8_15$logFC)))
)) +
ggtitle("PND15 vs PND8") +
labs(subtitle = "loess fit") +
xlab("Mean expression") +
ylab("Log2 Fold Change") +
theme(
plot.title = element_text(face = "bold", size = 20, hjust = 0.5),
plot.subtitle = element_text(size = 16, hjust = 0.5, face = "italic", color = "blue"),
axis.title.x = element_text(face = "bold", size = 15),
axis.text.x = element_text(face = "bold", size = 12),
legend.title = element_text(face = "bold", size = 15),
legend.text = element_text(size = 14)
) +
scale_colour_discrete(name = "p.adjusted < 0.05") +
stat_smooth(se = FALSE, method = "loess", color = "red", formula = y ~ x, size = 1)p1## Warning: Use of `res_8_15$AveExpr` is discouraged. Use `AveExpr` instead.## Warning: Use of `res_8_15$logFC` is discouraged. Use `logFC` instead.## Warning: Use of `res_8_15$AveExpr` is discouraged. Use `AveExpr` instead.## Warning: Use of `res_8_15$logFC` is discouraged. Use `logFC` instead.
PNW21 vs PND15
res_15_21 <- pnd15.pnw21
res_15_21$threshold <- as.factor(res_15_21$adj.P.Val < 0.05)
p2 <- ggplot(data = res_15_21, aes(
x = res_15_21$AveExpr,
y = res_15_21$logFC,
colour = threshold
)) +
geom_point(alpha = 0.5, size = 1.8) +
geom_hline(aes(yintercept = 0), colour = "blue", size = 1) +
ylim(c(
-ceiling(max(abs(res_15_21$logFC))),
ceiling(max(abs(res_15_21$logFC)))
)) +
ggtitle("PNW21 vs PND15") +
labs(subtitle = "loess fit") +
xlab("Mean expression") +
ylab("Log2 Fold Change") +
theme(
plot.title = element_text(face = "bold", size = 20, hjust = 0.5),
plot.subtitle = element_text(size = 16, hjust = 0.5, face = "italic", color = "blue"),
axis.title.x = element_text(face = "bold", size = 15),
axis.text.x = element_text(face = "bold", size = 12),
legend.title = element_text(face = "bold", size = 15),
legend.text = element_text(size = 14)
) +
scale_colour_discrete(name = "p.adjusted < 0.05") +
stat_smooth(se = FALSE, method = "loess", color = "red", formula = y ~ x, size = 1)p2## Warning: Use of `res_15_21$AveExpr` is discouraged. Use `AveExpr` instead.## Warning: Use of `res_15_21$logFC` is discouraged. Use `logFC` instead.## Warning: Use of `res_15_21$AveExpr` is discouraged. Use `AveExpr` instead.## Warning: Use of `res_15_21$logFC` is discouraged. Use `logFC` instead.
Heatmap of most variable genes
xvar <- apply((data$cpm + 1), 1, var)
genes500 <- head(sort(xvar, decreasing = TRUE), n = 500)
x500 <- data$cpm[rownames(data$cpm) %in% names(genes500), ]
pheatmap(x500,
scale = "row", show_rownames = F, col = viridis(100),
show_colnames = F, annotation_col = data$pData[, 1:2],
main = "Clustering of samples based on top 500 variable genes"
)
PCA
CPM
plgINS::plPCA(
x = data$cpm, samples_data = data$pData,
colorBy = "Group", shapeBy = "LibPrepBatch", add.labels = FALSE
)SVA normalized
plgINS::plPCA(
x = data$sva, samples_data = data$pData,
colorBy = "Group", shapeBy = "LibPrepBatch", add.labels = FALSE
)References
report::cite_packages(session = sessionInfo())## Warning in utils::citation(pkg_name): no date field in DESCRIPTION file of
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devtools::session_info() %>%
details::details()
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.4 (2021-02-15)
os Ubuntu 16.04.7 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Zurich
date 2021-06-28
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